Search

To evaluate a commercial tomosynthesis computer-aided detection (CAD) system in an independent, multicenter dataset.Diagnostic and screening tomosynthesis mammographic examinations (n = 175; cranial caudal and mediolateral oblique) were randomly selected from a previous institutional review board-approved trial. All subjects gave informed consent. Examinations were performed in three centers and included 123 patients, with 132 biopsy-proven screening-detected cancers, and 52 examinations with negative results at 1-year follow-up. One hundred eleven lesions were masses and/or microcalcifications (72 masses, 22 microcalcifications, 17 masses with microcalcifications) and 21 were architectural distortions. Lesions were annotated by radiologists who were aware of all available reports. CAD performance was assessed as per-lesion sensitivity and false-positive results per volume in patients with negative results.Use of the CAD system showed per-lesion sensitivity of 89% (99 of 111; 95% confidence interval: 81%, 94%), with 2.7 ± 1.8 false-positive rate per view, 62 of 72 lesions detected were masses, 20 of 22 were microcalcification clusters, and 17 of 17 were masses with microcalcifications. Overall, 37 of 39 microcalcification clusters (95% sensitivity, 95% confidence interval: 81%, 99%) and 79 of 89 masses (89% sensitivity, 95% confidence interval: 80%, 94%) were detected with the CAD system. On average, 0.5 false-positive rate per view were microcalcification clusters, 2.1 were masses, and 0.1 were masses and microcalcifications.A digital breast tomosynthesis CAD system can allow detection of a large percentage (89%, 99 of 111) of breast cancers manifesting as masses and microcalcification clusters, with an acceptable false-positive rate (2.7 per breast view). Further studies with larger datasets acquired with equipment from multiple vendors are needed to replicate the findings and to study the interaction of radiologists and CAD systems.

A strong association between breast arterial calcifications (BAC) and cardiovascular disease has been demonstrated. However, BAC quantification tools are lacking. We evaluated the intra- and inter-observer reproducibility of a semi-automatic tool for BAC quantification on digital mammograms. A multivendor image dataset of 212 mammographic views, 106 cranio-caudal (CC) and 106 medio-lateral oblique (MLO), were retrospectively selected from 53 subjects if BAC were seen in at least one view. Images were segmented twice by two intensively trained residents in Radiodiagnostics with > 6-month experience in mammography using a semi-automatic software. The two observers (O1, O2) independently positioned rectangular ROIs where they recognized BAC on both CC and MLO views, separately. The adaptive thresholding algorithm automatically provided the BAC amount in mm2. Number, size, and position of the ROIs were observer-dependent. Total BAC amount was calculated for each patient. Bland–Altman analysis was used. Total BAC amount was 56.6 (IQR 18.1–91.1) and 41.0 (IQR 18.8–90.9) for O1 and O2, respectively. Intra-observer Bland–Altman analysis showed a bias of 11.9 mm2, a coefficient of repeatability of 32.7 mm2, an average measurement of 72.8 mm2, for a 55% reproducibility; the same data were − 7.0, 61.4, 63.4 mm2, and only 3%, respectively, for the inter-observer analysis. Our semi-automatic tool for BAC quantification showed a poor reproducibility. These results pointed out that the human identification of BAC represents the main source of variability. Further research is needed to translate BAC quantification into clinical practice.

Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a radiological tool for the detection and discrimination of breast lesions. The aim of this study is to evaluate a computer-aided diagnosis (CAD) system for discriminating malignant from benign breast lesions at DCE-MRI by the combined use of morphological, kinetic, and spatiotemporal lesion features. Methods: Fifty-four malignant and 19 benign breast lesions in 51 patients were retrospectively evaluated. Images were acquired at two centers at 1.5T. Mass-like lesions were automatically segmented after image normalization and elastic coregistration of contrast-enhanced frames. For each lesion, a set of 28 3D features were extracted: ten morphological (related to shape, margins, and internal enhancement distribution); nine kinetic (computed from signal-to-time curves); and nine spatiotemporal (related to the variation of the signal between adjacent frames). A support vector machine (SVM) was trained with feature subsets selected by a genetic search. Best subsets were composed of the most frequent features selected by majority rule. The performance was measured by receiver operator characteristics analysis with a stratified tenfold cross-validation and bootstrap method for confidence intervals. Results: SVM training by the three separated classes of features resulted in an area under the curve (AUC) of 0.90 6 0.04 (mean 6 standard deviation), 0.87 6 0.06, and 0.86 6 0.06 for morphological, kinetic, and spatiotemporal feature, respectively. Combined training with all 28 features resulted in AUC of 0.96 6 0.02 obtained with a selected feature subset composed by two morphological, one kinetic, and two spatiotemporal features. Conclusions: Quantitative combination of morphological, kinetic, and spatiotemporal features is feasible and provides a higher discriminating power than using the three different classes of features separately. V C 2012 American Association of Physicists in Medicine.

OBJECTIVE To establish whether muscle blood flow (MBF) measurements with O-water positron emission tomography could reliably identify patients with critical limb ischemia and detect and quantify a distal deficit in skeletal MBF in these cases. DESIGN O-water positron emission tomography scans were performed at rest or during unloaded ankle plantar and dorsiflexion exercise of the diseased leg in 17 subjects with leg ischemia or on a randomly selected leg of 18 age-matched healthy control subjects. TcPO2 was evaluated with Novametrix monitors and perfusion of skin topically heated to 44 degrees C and adjacent nonheated areas with a Moor Instruments laser Doppler imaging scanner. RESULTS The enhancement of MBF induced by exercise was significantly lower in ischemic than in normal legs, and the sensitivity and specificity of this phenomenon were similar to those of laser Doppler imaging or TcPO2 in identifying ischemia subjects. In addition, the exercise MBF deficit was predominant at the distal-leg levels, indicating the ability of the technique to help determine the correct level of amputation. CONCLUSIONS Skeletal MBF of legs with severe ischemia can be detected accurately with O-water positron emission tomography and could add valuable information about viability of skeletal muscle in the residual limb when deciding the level of an amputation.

To prospectively evaluate regional alterations in the apparent diffusion coefficient (ADC) of cortical gray and white matter and subcortical structures that are known to be involved in mild cognitive impairment (MCI).Magnetic resonance (MR) imaging was performed in 13 patients with MCI (nine men, four women; mean age, 74 years +/- 6 [standard deviation]) and 13 healthy elderly control subjects (seven men, six women; mean age, 75 years +/- 4). This study was approved by the institutional review board and was HIPAA compliant. Each subject gave informed consent. ADC was measured from manually drawn regions of interest (ROIs) of the hippocampus, parahippocampal gyrus, amygdala, corpus callosum, and anterior and posterior cingulate gyrus and from automatically defined frontal, parietal, occipital, and temporal lobes by using template masking. ROIs were outlined on anatomic images then mapped onto ADC maps by using coregistration transformation matrix. A skeleton-based region competition segmentation algorithm was used for segmentation of gray and white matter. The group difference in ADC values was assessed with independent-sample t tests. Pearson correlation analysis was used to examine the correlation of ADC values with age and memory test scores.Higher ADCs were found in hippocampus, temporal lobe gray matter, and corpus callosum of patients with MCI compared with that of control subjects (P.05). By pooling all subjects together, an elevated hippocampal ADC was significantly correlated with worse memory performance scores in 5-minute and 30-minute delayed word-list recall tasks (P.05).ADCs from gray and white matter of different brain regions can be analyzed by applying an automated template-masking method in conjunction with a skeleton-based region competition segmentation algorithm.

To compare breast density (BD) assessment provided by an automated BD evaluator (ABDE) with that provided by a panel of experienced breast radiologists, on a multivendor dataset.Twenty-one radiologists assessed 613 screening/diagnostic digital mammograms from nine centers and six different vendors, using the BI-RADS a, b, c, and d density classification. The same mammograms were also evaluated by an ABDE providing the ratio between fibroglandular and total breast area on a continuous scale and, automatically, the BI-RADS score. A panel majority report (PMR) was used as reference standard. Agreement (κ) and accuracy (proportion of cases correctly classified) were calculated for binary (BI-RADS a-b versus c-d) and 4-class classification.While the agreement of individual radiologists with the PMR ranged from κ = 0.483 to κ = 0.885, the ABDE correctly classified 563/613 mammograms (92 %). A substantial agreement for binary classification was found for individual reader pairs (κ = 0.620, standard deviation [SD] = 0.140), individual versus PMR (κ = 0.736, SD = 0.117), and individual versus ABDE (κ = 0.674, SD = 0.095). Agreement between ABDE and PMR was almost perfect (κ = 0.831).The ABDE showed an almost perfect agreement with a 21-radiologist panel in binary BD classification on a multivendor dataset, earning a chance as a reproducible alternative to visual evaluation.Individual BD assessment differs from PMR with κ as low as 0.483. An ABDE correctly classified 92 % of mammograms with almost perfect agreement (κ = 0.831). An ABDE can be a valid alternative to subjective BD assessment.

OBJECTIVES The objective of this study was to prospectively compare diagnostic performance and time efficiency of a double-reading paradigm in which a first-reader computer-aided detection (CAD) is followed by a fast 2-dimensional review (DR FR-CAD) with those of a double reading with second-reader CAD (SR CAD). MATERIALS AND METHODS The local ethical committee approved this study. Consecutive immunological patients who have positive results for fecal immunological test who were scheduled for colonoscopy were enrolled for a 10-month period. Computed tomographic colonography studies were read with CAD (CAD COLON-1.20; im3D, Turin, Italy) by using both SR CAD (applied after unassisted interpretation primary 2-dimensional) and DR FR-CAD (CAD-prompts evaluation followed by a fast 2-dimensional review) in randomized order with the radiologist for each reading paradigm masked to the other reader's results.Per-patient sensitivity and specificity of unassisted and CAD-assisted readings for detecting 6-mm adenomas or larger were calculated by using unblinding colonoscopy as reference standard. Reporting times were also calculated. Pairwise comparisons were performed. RESULTS A total of 182 participants (median age, 65 years; range, 58-76) were included in the final analysis. Of these, 93 (51%) had at least 1 cancer or a 6-mm adenoma or larger. At the 6-mm threshold, sensitivity of unassisted reading (79.6%; 95% confidence interval [CI], 69.9-87.2) increased significantly with the use of both SR CAD (86.0%; 95% CI, 77.3%-92.3%) and DR FR-CAD (89.2%; 95% CI, 81.1%-94.7%), without differences between CAD readings (P = 0.500). No significant differences in specificity among the 3 paradigms were observed. Double reading with first-reader CAD required less reading time than that for SR CAD (378 vs 496; Δ118 seconds; P < 0.001) and was 59 seconds longer than the unassisted reading (P = 0.058). CONCLUSIONS When compared with unassisted reading, a double-reading paradigm in which first-reader CAD is followed by a fast 2-dimensional review improves the adenoma detection rate to the same level achieved by a second-reader CAD while decreasing reporting times.

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and “best practices” for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice.

Central sensitization and dysregulation of peripheral substance P and neurokinin-1 receptor (NK-1R) signaling are associated with chronic abdominal pain in inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). Although positron emission tomography (PET) has demonstrated that patients with injury-related chronic pain have diminished NK-1R availability in the brain, it is unknown whether these deficits are present in IBD and IBS patients, who have etiologically distinct forms of non-injury-related chronic pain. This study's aim was to determine if patients with IBD or IBS exhibit deficits in brain expression of NK-1Rs relative to healthy controls (HCs), the extent to which expression patterns differ across patient populations, and if these patterns differentially relate to clinical parameters. PET with [(18)F]SPA-RQ was used to measure NK-1R availability by quantifying binding potential (BP) in the 3 groups. Exploratory correlation analyses were performed to detect associations between NK-1R BP and physical symptoms. Compared to HCs, IBD patients had NK-1R BP deficits across a widespread network of cortical and subcortical regions. IBS patients had similar, but less pronounced deficits. BP in a subset of these regions was robustly related to discrete clinical parameters in each patient population. Widespread deficits in NK-1R BP occur in IBD and, to a lesser extent, IBS; however, discrete clinical parameters relate to NK-1R BP in each patient population. This suggests that potential pharmacological interventions that target NK-1R signaling may be most effective for treating distinct symptoms in IBD and IBS.

OBJECTIVES The objectives of this study were to develop a fully automatic method for detecting blood vessels in dynamic contrast-enhanced magnetic resonance imaging of the breast on the basis of a multiscale 3-dimensional Hessian-based algorithm and to evaluate the improvement in reducing the number of vessel voxels incorrectly classified as parenchymal lesions by a computer-aided diagnosis (CAD) system. MATERIALS AND METHODS The algorithm has been conceived to work on images obtained with different sequences, different acquisition parameters, such as the use of fat-saturation, and different contrast agents. The analysis was performed on 28 dynamic contrast-enhanced magnetic resonance imaging examinations, with 39 malignant (28 principal and 11 satellite) and 8 benign lesions, acquired at 2 centers using 2 different 1.5-T magnetic resonance scanners, radiofrequency coils, and contrast agents (14 studies from group A and 14 studies from group B). The method consists of 2 main steps: (a) the detection of linear structures on 3-dimensional images, with a multiscale analysis based on the second-order image derivatives and (b) the exclusion of non-vessel enhancements based on their morphological properties through the evaluation of the covariance matrix eigenvalues. To evaluate the algorithm performances, the identified vessels were converted into a 2-dimensional vasculature skeleton and then compared with manual tracking performed by an expert radiologist. When assessing the outcome of the algorithm performances in identifying vascular structures, the following terms must be considered: the correct-detection rate refers to pixels identified by both the algorithm and the radiologist, the missed-detection rate refers to pixels detected only by the radiologist, and the incorrect-detection rate refers to pixels detected only by the algorithm. The Wilcoxon rank sum test was used to assess differences between the performances of the 2 subgroups of images obtained from the different scanners. RESULTS For the testing set, which is composed of 28 patients from 2 different clinical centers, the median correct-detection rate was 89.1%, the median missed-detection rate was 10.9%, and the median incorrect-detection rate was 27.1%. The difference between group A and group B was not significant (P > 0.25). The exclusion of vascular voxels from the lesion detection map of a CAD system leads to a reduction of 68.4% (30.0%) (mean [SD]) of the total number of false-positives because of vessels, without a significant difference between the 2 subgroups (P = 0.50). CONCLUSIONS The system showed promising results in detecting most vessels identified by an expert radiologist on both fat-saturated and non-fat-saturated images obtained from different scanners with variable temporal and spatial resolutions and types of contrast agent. Moreover, the algorithm may reduce the labeling of vascular voxels as parenchymal lesions by a CAD system for breast magnetic resonance imaging, improving the CAD specificity and, consequently, further stimulating the use of CAD systems in clinical workflow.

Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. Compared with previous methods based on thresholding, this method is more robust to noise and field inhomogeneities. The method was quantitatively evaluated on 31 cases acquired from two centers by comparing the results with a manual segmentation. Results indicate good overall agreement within the reference segmentation (overlap=0.79 ± 0.09, recall=0.95 ± 0.02, precision=0.82 ± 0.1).

Purpose: To describe and test a new fully automatic lesion detection system for breast DCE-MRI. Materials and Methods: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrast-enhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (system-detected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed. Results: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5–15 mm) for benign and 26 mm (range, 5–75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%–95%) and sensitivity was 52/53 (98%; 95% CI 90%–99%). Mammary median FPs per breast was 4 (1st–3rd quartiles 3–7.25). Conclusion: The system showed promising results on MR datasets obtained from different scanners producing fat-sat or non–fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice. J. Magn. Reson. Imaging 2011;. © 2011 Wiley Periodicals, Inc.

The aim of this study was to compare the computed tomographic colonography (CTC) image quality and patient acceptance of three iodine-based faecal tagging bowel preparations in 60 patients undergoing the following regimens: a 2-day regimen of meal-time administration of iodine and phospho-soda (GFPH); a 2-day regimen of meal-time mild laxative, followed by iodine administered 2 h before CTC (SD); and a 2-day regimen of meal-time administration of iodine (GF).Two independent radiologists assessed tagging quality; quantitative measures included the tagged stool density, and computer-aided detection (CAD) false-positive rate.The GFPH and SD regimens provided better subjective quality than GF (p0.001). The latter regimen resulted in a higher proportion of insufficiently tagged segments: the measured average stool density was less than 200 HU in 10.7% in all segments vs 3.6% for SD and0.5% for GFPH, respectively. Insufficient tagging occurred mostly in the ascending colon and the caecum. The CAD false-positive rate increased following the trend: GFPHSDGF (p = 0.00012). GFPH was worse tolerated than SD (p0.05).Considering preparation quality alone, GFPH was the best regimen, but SD provided the best balance between bowel preparation quality and patient acceptability.

Regional differences in tissue volume and perfusion in brains of individuals with mild cognitive impairment (MCI) versus normal healthy age-matched controls (NC), and the differences between MCI-AD converters and stable MCI patients were investigated. MRI and SPECT scans were performed on 13 MCI (74+6 years) and 12 NC (75+4 years). Of the MCI patients, 10 were followed for up to three years and 4 subsequently converted to Alzheimer's disease (AD). Episodic memory function was assessed using tests of delayed recall for word lists and stories. The volume reductions and hypoperfusion were mainly confined to the medial temporal lobe (MTL) of MCI patients and associated with worse scores on memory tests. Perfusion in the corpus callosum and the gray matter of frontal, lateral temporal, parietal or occipital lobe was not significantly affected in MCI. The 4 MCI-AD converters had relatively low MTL structural volume and perfusion compared to their stable peers.

An automatic method for the segmentation of the colonic wall is proposed for abdominal computed tomography (CT) of the cleansed and air-inflated colon. This multistage approach uses an adaptive 3D region-growing algorithm, with a self-adjusting growing condition depending on local variations of the intensity at the air-tissue boundary. The method was evaluated using retrospectively collected CT scans based on visual segmentation of the colon by expert radiologists. This evaluation showed that the procedure identifies 97% of the colon segments, representing 99.8% of the colon surface, and accurately replicates the anatomical profile of the colonic wall. The parameter settings and performance of the method are relatively independent of the scanner and acquisition conditions. The method is intended for application to the computer-aided detection of polyps in CT colonography.

The successful application of Computer Aided Detection schemes to CT Colonography depends not only on their performances in terms of sensitivity and specificity, but also on the interaction with the radiologist, and thus ultimately on factors such as the nature of CAD prompts and the reading paradigm. Fecal tagging is emerging as a widely accepted technique for patient preparation, and patient-friendlier schemes are being proposed in an effort to increase compliance to screening programs; the interaction between CAD and FT regimens should likewise be taken into account. In this scenario, an analysis of the characteristics of CAD prompts is of paramount importance in order to guide further research, both from clinical and technical viewpoints. The CAD scheme analyzed in this paper is essentially composed of five steps: electronic cleansing, colon surface extraction, polyp candidate segmentation, pre-filtering of residual tagged stool and classification of the generated candidates in true polyps vs. false alarms. False positives were divided into six categories: untagged and tagged solid stool, haustral folds, extra-colonic candidates, ileocecal valve and cleansing artifacts. A full cathartic preparation was compared with a semi-cathartic regimen with sameday fecal tagging, which is characterized by higher patient acceptance but also higher inhomogeneity. The distribution of false positives at segmentation reflects the quality of preparation, as more inhomogeneous tagging results in a higher number of untagged solid stool and cleansing artifacts.

Dynamic Contrast Enhanced MRI (DCE-MRI) has today a well-established role, complementary to routine imaging techniques for breast cancer diagnosis such as mammography. Despite its undoubted clinical advantages, DCE-MRI data analysis is time-consuming and Computer Aided Diagnosis (CAD) systems are required to help radiologists. Segmentation is one of the key step of every CAD image processing pipeline, but most techniques available require human interaction. We here present the preliminary results of a fully automatic lesion detection method, capable of dealing with fat suppression image acquisition sequences, which represents a challenge for image processing algorithms due to the low SNR. The method is based on four fundamental steps: registration to correct for motion artifacts; anatomical segmentation to discard anatomical structures located outside clinically interesting lesions; lesion detection to select enhanced areas and false positive reduction based on morphological and kinetic criteria. The testing set was composed by 13 cases and included 27 lesions (10 benign and 17 malignant) of diameter > 5 mm. The system achieves a per-lesion sensitivity of 93%, while yielding an acceptable number of false positives (26 on average). The results of our segmentation algorithm were verified by visual inspection, and qualitative comparison with a manual segmentation yielded encouraging results.

The crystalline orbitals of seven oxygen containing compounds with increasing degree of covalent character (MgO, MnO, ZnO, Al2O3, SiO2, AlPO4, and CaSO4) are localized according to a Wannier–Boys mixed scheme recently implemented. The resulting Wannier functions are analyzed in terms of various indices (centroids positions, second-order central moment tensor, its eigenvalues and principal axes, Mulliken population analysis, and atomic localization indices). Systematic trends are observed along the series.

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and “best practices” for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice.

The crystalline orbitals of six semiconductors (Si, C, BP, AlP, SiC and BN) are localized according to a Wannier–Boys mixed scheme recently implemented. The degree of localization, size and shape of the localized crystalline orbitals are documented by means of various indices (centroid position, second order central moment tensor, its eigenvalues and principal axes, Mulliken population analysis and atomic localization indices) and through their graphical representations. Systematic trends along the series are observed, and are correlated to the Pauling electronegativity scale and the calculated band gap.